Training

Code

Training <- read_csv("Training2022_v2.csv", show_col_types = FALSE)

I can resume all this learning experience as “Discipline”. Motivation makes you start discipline moves you to the end.

1 Training per Area

What you will see below is a list of training I did to support my professional life as an Information technology Project Manager. When you work as a consultant in the enterprise is important to know about the best practices in IT Governance, Project Management and the trending technologies.

Note

Not to micromanage but to see what others can’t, pro actively avoiding problems and identifying risks. “You can’t control what you can’t measure” – Tom DeMarco.

Code

GA<- Training %>% 
ggplot(aes(y = fct_infreq(Area))) +
 geom_bar(aes(fill = fct_infreq(Area)),
          stat = "count") +
 ggtitle("Training") +
 xlab("Number of Courses") +
 scale_fill_brewer(palette = "Blues", direction = -1 ) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

2 Information Technology Governance

Know IT Governance is important to feel the context we are inserted. Projects must be Planned, Executed, Controlled and Closed with this in mind. At the end the project must fit in the Governance in place considering, people, polices and frameworks, processes, organizational structures, culture, ethics and behaviour, information, services, Infrastructure and applications.

Code

# Filtering Training by Area
Governance <- Training %>%
  filter( Area == "IT Governance") %>%
  arrange(Skills, Course,"Date", descending = TRUE) %>%
  select(Course, Skills,'Date')

GA<- Governance %>% 
ggplot(aes(y = fct_infreq(Skills))) +
 geom_bar(aes(fill = fct_infreq(Skills)),
          stat = "count") +
 ggtitle("IT Governance Training") +
 xlab("Number of Courses") +
 scale_fill_brewer(palette = "Purples", direction = -1 ) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

Figure 1: COBIT & ITIL Certificate

Code

# Building a Table 
Governance %>% datatable(class = 'cell-border stripe',filter = "top", 
          options = list(pageLength = 7),
          caption = htmltools::tags$caption( 
            style = 'caption-side: top;
                     text-align: left;
                     color:black;
                     font-size:100% ;',
            'IT Governance Training'))

3 Project Management

Project Management is about Servant Leadership it means be open to do your best to remove any impediments we as a team have to deliver the project outcomes.

The best practices like PRINCE2, PMBOK, and agile practices like Scrum, Kanban, RUP, facilitates the delivery process.

Note

Lets keep in mind we are free to adapt and improve methodologies and project documentations to fit our company culture. “Methodologies are treks not rails” - Joao Luiz D’Andrea

Code

ProjectManagement <- Training %>%
  filter( Area == "Project Management") %>%
  arrange(Skills,Technology,Course,"Date", descending = TRUE) %>%
  select(Course, Skills, 'Date')

GA<- ProjectManagement %>% 
ggplot(aes(y = fct_infreq(Skills))) +
 geom_bar(aes(fill = fct_infreq(Skills)),
          stat = "count") +
 ggtitle("Project Management Training") +
 xlab("Number of Courses") +
 scale_fill_brewer(palette = "Blues", direction = -1 ) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

Figure 2: PRINCE2 & Managing Successfull Programmes Certification

Code

ProjectManagement %>% datatable(class = 'cell-border stripe',filter = "top", 
          options = list(pageLength = 50),
          caption = htmltools::tags$caption( 
          style = 'caption-side: top;                                                                           text-align: left;                                                                          color:black;
                   font-size:100%;
          ','Project Management Training'))

4 Information Technology and Data Science Training

4.1 Information Technology

I love technology and how it affect the business and our day-by-day activities and change the world. That was my motivation when I started my way in IT.

It has been a life learning endeavour to keep up to date and have a deep understand of new technologies in special in the last ten year when the changes on infrastructure enable Artificial Intelligence, Machine Learning, Block Chain and the Cloud movement.

In the table below we can see my effort in that direction learning: Spark (Spark SQL, Spark Graph-X, Spark ML, Spark Streaming), Hadoop, Ambary, HDFS, Map Reduce, Pig, Hive, Drill, Phoenix, Presto, Yarn, Messos, Zookeeper, Oozy, Zeppelin, Hue, Flume, Sqoop, NIFI, Kafka, Node-Red. No Relational Databases like: Hbase, MongoDB, Cassandra.

Update with the new releases of ERP and CRM Software like SAP, Sales-force, Microsoft Dynamics 365 and Cloud Environment: Amazon-AWS, Google Cloud, Databricks, AZURE.

Code

IT <- Training %>%
  filter(Area == "Information technology") %>% 
  arrange(Skills,Technology,Course,"Date", descending = FALSE) %>%
  select(Course, Skills, Technology,'Date')

GA<- IT %>% 
ggplot(aes(y = fct_infreq(Skills))) +
 geom_bar(aes(fill = fct_infreq(Skills)),
              stat = "count") +
 ggtitle("Training by IT Skill") +
 xlab("Number of Courses") +
 scale_fill_viridis(discrete = TRUE) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

Code

IT %>% datatable(class = 'cell-border stripe',filter = "top", 
          options = list(pageLength = 50),
          caption = htmltools::tags$caption( 
          style = 'caption-side: top;
                   text-align: left;
                   color:black;
                   font-size:100%;', 'Information Technology Training'))

Code

GA<- IT %>% 
ggplot(aes(y = fct_infreq(Technology))) +
 geom_bar(aes(fill = fct_infreq(Technology)),
          stat = "count") +
 ggtitle("Training by Technology") +
 xlab("Number of Courses") +
 scale_fill_viridis(discrete = TRUE) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

4.2 Data Science & Big Data

In this section we can see the training I did to deep dive in Artificial Intelligence and Machine Learning. Writing code in R, Python using tools like Spark, Kera’s, Theano, TensorFlow, H20, Tableau, Power BI.

Machine Learning Algorithms for :

Regression: Simple Linear Regression, Multiple Linear Regressions, Polynomial Regression, Support Vector Regression (SVR), Decision Tree, Random Forest, XGBoost, Catboost, Lightgbm.

Classification: Logistic Regression, K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Kernel SVM, Naïve Bayes, Decision Tree Classification, Random Forests Classification, XGBoost, Cat Boost.

Clustering: K-Means Clustering, Hierarchical Clustering.

Association Rule (Basket Analysis): Apriori, Ecla.

Reinforcement Learning: Upper Confidence Bound (UCB), Thompson Sampling, QQ-Learning. NLP - Natural Language Processing.

Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel (PCA),

Model Selection and Boosting: K-Fold Cross Validation, Grid Search.

Artificial Intelligence: Computer Vision, Object Recognition and reinforcement Learning. Q-Learning, Convolution Neural Networks, YOLO5.

Code

DataScience <- Training %>%
  filter( Area == "Data Science") %>%
  arrange(Skills,Technology,Course,"Date", descending = FALSE) %>%
  select(Course, Skills, Technology, 'Date')

ITDS <- Training %>%
  filter(Area == "Data Science") %>% 
  arrange(Skills,Technology,Course,"Date", descending = FALSE) %>%
  select(Course, Skills, Technology, 'Date')

GA<- ITDS %>% 
ggplot(aes(y = fct_infreq(Skills))) +
 geom_bar(aes(fill = fct_infreq(Skills)),
          stat = "count") +
 ggtitle("Training by IT Skill") +
 xlab("Number of Courses") +
 scale_fill_viridis(discrete = TRUE) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

Code

DataScience %>% datatable(class = 'cell-border stripe',filter = "top", 
          options = list(pageLength = 50, autoWidth = TRUE),
          caption = htmltools::tags$caption( 
          style = 'caption-side: top;
                   text-align: left;
                   color:black;
                   font-size:100% ;                                                      '                      ,'Data Science & Big Data Training')) 

Code

GA<- ITDS %>% 
ggplot(aes(y = fct_infreq(Technology))) +
 geom_bar(aes(fill = fct_infreq(Technology)),
          stat = "count") +
 ggtitle("Training by Technology") +
 xlab("Number of Courses") +
 scale_fill_viridis(discrete = TRUE) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

5 Business & Management

This is a couple of training in Business and Management like Marketing, Sales, Productivity, Team Building and related soft skills.

Code

Business <- Training %>%
  filter( Area == "Business") %>%
  arrange(Skills,Course,"Date", descending = TRUE) %>%
  select(Course, Skills, 'Date')

GA<- Business %>% 
ggplot(aes(y = fct_infreq(Skills))) +
 geom_bar(aes(fill = fct_infreq(Skills)),
          stat = "count") +
 ggtitle("Business Training") +
 xlab("Number of Courses") +
 scale_fill_viridis(discrete = TRUE) +
 theme(legend.position = "none") 

ggplotly(GA, tooltip = "count") %>% 
  config(displayModeBar = FALSE)

Code

Business %>% datatable(class = 'cell-border stripe',filter = "top", 
          options = list(pageLength = 50, autoWidth = TRUE),
          caption = htmltools::tags$caption( 
          style = 'caption-side: top;
                   text-align: left;
                   color:black;
                   font-size:100%;' ,'Business and Management'))